o .wi @sXddlmZddlZddlmZmZddlmZd dededeed ed ef d d Z dS))OptionalN)Tensortensor)"_check_retrieval_functional_inputsFpredstargettop_k adaptive_kreturncCst||\}}t|tstd|dus|r"||jdkr"|jd}t|tr+|dks/td|s:td|jdSt |dk|t |}||j t ||jdddd }||S) aCompute the precision metric for information retrieval. Precision is the fraction of relevant documents among all the retrieved documents. ``preds`` and ``target`` should be of the same shape and live on the same device. If no ``target`` is ``True``, ``0`` is returned. ``target`` must be either `bool` or `integers` and ``preds`` must be ``float``, otherwise an error is raised. If you want to measure Precision@K, ``top_k`` must be a positive integer. Args: preds: estimated probabilities of each document to be relevant. target: ground truth about each document being relevant or not. top_k: consider only the top k elements (default: ``None``, which considers them all) adaptive_k: adjust `k` to `min(k, number of documents)` for each query Returns: A single-value tensor with the precision (at ``top_k``) of the predictions ``preds`` w.r.t. the labels ``target``. Raises: ValueError: If ``top_k`` is not `None` or an integer larger than 0. ValueError: If ``adaptive_k`` is not boolean. Example: >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_precision(preds, target, top_k=2) tensor(0.5000) z `adaptive_k` has to be a booleanNrz,`top_k` has to be a positive integer or Noneg)device)dim)r isinstancebool ValueErrorshapeintsumrr torchwhere zeros_liketopkminfloat)rrrr target_filteredrelevantrh/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/retrieval/precision.pyretrieval_precisions  *r)NF) typingrrrrtorchmetrics.utilities.checksrrrrrrrrs  (